A New Venture Performance Model in the Korean
Information and Telecommunications Industry
Myeong-Cheol Park and Sang-Woo Lee
In this paper, we investigate on the determinants of new
venture performance empirically and suggest a new venture performance model in the Korean Information and
Telecommunications (IT) industry. A total of seven hypotheses is established and tested using a combination of factor analysis, cluster analysis, analysis of variance, and correlation analysis. Ninety-two sample ventures in the Korean
IT industry were investigated and analyzed to test hypotheses. We found that new ventures performance depends on
their environment, strategy, internal resources factors, and,
most importantly, two-way and three-way interactions of
these factors. These findings could be interpreted as supporting the general strategy theory that when environment
and internal resources are fitted, performance might be
maximized and, further, strategy is the means that make
internal resources and environment fit. The venture performance model presented in this paper can explain how
new ventures achieve their successes. The strategic implications to the venture firms are also discussed.
Manuscript received June 15, 2000; revised October 31, 2000.
The authors are with School of Management, Information and Communications University,
Teajon, Korea.
Myeong-Cheol Park (phone: +82 42 866 6300, e-mail: mcpark@icu.ac.kr)
Sang-Woo Lee (phone: +82 42 866 6323, e-mail: woody71@icu.ac.kr)
ETRI Journal, Volume 22, Number 4, December 2000
I. INTRODUCTION
New ventures are the important part of the healthy economy.
They are vital to the national economy, with most of the net
new jobs created by new venture, especially in Korea. Recently,
the formation of new ventures in Korea is increasing. However,
according to The Wall Street Journal, the failure rate of new
ventures is substantial and more than 62.7 % of all new companies fail by their sixth year, most of those in the first three
years of existence in the U.S. [1].
Despite the importance of new ventures, prior research and
theory on how new ventures achieve success are incomplete.
Therefore, the purpose of this study is to seek to answer the
question: In what ways and to what extent do strategy, environment, and the internal resources influence new venture performance for very rapidly growing new ventures? Also, based
on empirical findings, this study provides venture entrepreneurs with business strategies more suitable for their environments and further forms a foothold for creating new ventures
and fostering their growth by presenting a guideline to assist
new ventures in building their business strategies.
The research definition for the phrase “new venture in the IT
industry” for this study is based on prior research in the field of
venture strategy and entrepreneurship. The research of Biggadike has been widely cited for its contribution to delineating
the stages of development for a young business. He pointed out
that it takes an average of 10 to 12 years before the Return On
Investment (ROI) of ventures equals that of mature business
[2]. Miller and Camp also identified the eight years that a company was in business as the “start-up” phase of the venture, and
the next four years as the “adolescent” phase [3]. Consistent
with their prior research, this research also used the age of eight
years to define ventures that would be classified as “New”. In
Myeong-Cheol Park et al.
51
addition to defining a venture by age, this research was only
concerned with the Korean IT industry.
This study is organized into six sections in order to examine
the determinants of new venture performance, build the venture performance model and find out what strategy ventures in
the Korean IT industry should adopt for the competitive advantage. First section provides a general overview. The second section reviews the relevant literature in determinants of venture
performance and model of new venture performance. Section
three develops and discusses the research questions and hypotheses addressed in this study. Section four presents the empirical results of the determinants of environments, strategy and
internal resource types. The test results of the hypotheses are
presented for each of the multiple dependent variables used in
the study. Section five suggests the appropriate strategies suitable for the environmental conditions and internal resources to
venture entrepreneurs based on the test results. Finally, section
six concludes the study with a summary of the research findings and a discussion of the contribution. Also the limitations of
this study for future research are presented.
II. LITERATURE REVIEW
1. Determinants of New Venture Performance and the
Current Model of New Venture Performance
The researches focusing on the determinants of new venture
performance have been mostly developed since 1980. Traditional academic literature in the field of new ventures has concentrated on the characteristics of entrepreneur as the primary
determinant of new ventures. Especially, most of the beginning
studies on the venture performance were focused on three aspects: the entrepreneurship, industry structure and strategy.
The traditional academic model of new venture performance
was a simple model that contains only one variable. However,
recent researches on the venture performance tend to explain
the venture performance in view of multi-dimension such as
entrepreneurs, industry structure, strategy, and interaction of
each other because of limits of single dimension’s explanation.
Sandberg developed a venture performance model based on
the venture capitalist’s performance model through the empirical examination of 17 (both successful and unsuccessful) venture companies. In his research, he found that industry structure
and venture strategy are the strongest determinants of new ventures, and the interaction of these measures also influence the
1)
venture performance, i.e., NVP = f(E, IS, S) [4]. This model
was empirically demonstrated by McDougall and Kunkel [5],
[6]. However, Sandberg’s findings were not able to find the re-
1) NVP: New Venture Performance, E: Entrepreneur, IS: Industry Structure, S: Strategy
52
Myeong-Cheol Park et al.
lationship between the characteristics of entrepreneur and new
venture performance. Chrisman, Bauerschmidt, and Hofer
suggested that Sandberg’s model of new venture performance
model should be extended to include the resources and the organizational structure, process, and systems developed by the
venture to implement its strategy and achieve its goals [7].
Timmons suggested a framework of entrepreneur’s desirable
characteristics and behaviors for the success [8]. Stuart & Abetti
suggested environments and strategy as factors explaining new
venture performance as well as characteristics, experience, and
ability of entrepreneur [9].
In overall, the results of these empirical studies on the venture
performance tend to indicate that entrepreneurship, strategy, industry structure, and their interaction are major determinants of
new venture performance. In summary, the new venture literature suggests several observations about strategy, environment,
and new venture performance. First, there is mixed evidence in
the new ventures empirical literature as to the single effects of
strategy or environment on performance. Second, some evidence suggests that the interaction between the new venture’s
strategy and its environment is important.
Based on the results of the prior studies on the determinants
of new venture performance, we can summarize the venture
performance model as following:
2)
NVP = f(VS, IS, VS*IS) .
2. Limitations of Existing Research.
Despite various results of studies on determinants of venture
performance, none of the prior studies has empirically identified factors other than venture strategy and industry structure.
To our knowledge, there seems no consensus as to other determinants than such two factors. Cooper & Gascon explained
the reasons as follows [10]:
First, due to the diversity of sample in venture age and industry sector, it is difficult to understand situational relation of
variables and also most researches on the new venture have
concentrated on cross-sectional studies, so it is difficult to generalize the result of researches.
Second, previous researches have utilized various methods
for evaluating venture performance, but the relation between
determinants and performance can be changed by varying the
performance measure. Market measures such as market share,
frequently used in finance or strategy researches are inadequate
in evaluating venture performance. Accounting information
such as return on investment and return on sales cannot be easily obtained and influenced by entrepreneurs and features of the
2) IS: Industry Structure, VS: Venture Strategy, VS*IS: Interaction between two factors.
ETRI Journal, Volume 22, Number 4, December 2000
industry. Sales or the number of employees is often used for
evaluating venture performance, but since the growth potentiality is different from one another and the evaluation on performance depends on entrepreneur’s point of view, there is no relation between subjective and objective evaluation in prior research [11]. On the other hand, Gregory Dess and Richard
Robinson found that there are highly correlation between objective data and subjective measures on return on assets, sales
growth, and global performance. They suggested that subjective
measures should also be useful to access broader organizational
and environmental performance determinants [12]. Kenneth
Robinson suggested that future researchers should utilize multiple measures of firm performance since alternatives measures
of firm performance are not necessarily interchangeable proxies for one another [13].
Finally, most of the earlier researches often utilized crosstabulation or univariate analysis. It is only recently that much of
the research has utilized the multivariate analysis that allows
the impact of interaction between different factors. In spite of
these various results of venture performance model, there is
some doubt whether prior venture performance model can be
applicable to the Korean IT firms because venture business
environment in Korea is very different from that in U.S.
III. RESEARCH QUESTIONS AND HYPOTHESES
1. The Beginning Model of This Research
In this study, we propose a new venture performance model
by including new factors which have not been examined in
prior researches and internal resources which can be controlled
by entrepreneur, as well as venture strategy and industry structure which were considered in the existing model.
The research design used in this study can be presented by
the following model:
NVP = f(VS,VR, VE)
VE: Environment Factors,
VR: Internal Resources Factors,
VS: Strategy Factors
In order to investigate these groups of factors comprehensively, we first analyzed individual hypotheses considering each
group of factors separately: strategy, environment, and internal
resources. These are followed by hypotheses accommodating
all possible interaction effects of strategy, environment, and internal resources. Thus, a total of seven hypotheses including
three sub-hypotheses will be analyzed.
ETRI Journal, Volume 22, Number 4, December 2000
2. Hypotheses
A. The Influence of Environment on New Venture Performance:
NVP = f (VE)
All the economic entities including new ventures are subject
to be influenced by environmental conditions such as industry
structure or regulatory policy. Particularly, in case of IT industry,
it is thought that new venture performance is more sensitive
and more dependent on the change of environmental conditions due to the special characteristics of IT industry. IT environment has many aspects: Competition, Technology, Product,
and Market. Based on this conceptualization of the environment, we hypothesize as following:
Hypothesis 1: New venture performances are subject to be influenced by environmental conditions only:
New Venture Performance (NVP) = f (Environment)
B. The Influence of Strategy on New Venture Performance:
NVP = f (VS)
Until now, many researchers have tried to find out the relationship between strategy and new venture economic performance. According to Broom and Longenecker’s research, it is
more effective for ventures to focus on the niche market rather
than to compete with incumbent firms for maximization of
their performance [14]. However, some recent researches suggested different results. That is, focusing strategy might be ineffective for maximization of new venture performance. Due to
the diverse results of prior studies on relationship between
strategy and performance, the influence of strategy on the venture performance has not been empirically concluded.
Thus, we hypothesize that the performance for a new venture
will differ across the strategy types.
Hypothesis 2: Performance for a new venture will differ by its
strategy types only:
New Venture Performance (NVP) = f (Strategy)
C. The Influence of Internal Resources on New Venture
Performance: NVP = f (VR)
Starting from the Selznick’s research concerning the internal
resources, Selznick defined internal resources as a “Distinctive
Competence” [15]. The concept of resource capability was
embodied by many researchers such as Hofer & Schendel,
Rumelt and Barney. Barney defined internal resources as a
“Firm Resources” [16] and Grant as a “Resource Capability”.
Myeong-Cheol Park et al.
53
According to the resource-based theory, internal resources are up” phase of the industry, but on “adolescent” phase, focusing
found to be the most important factors on new venture per- strategy is more useful rather than differentiation.
formance [17].
Based on these assumptions about venture performance, we
In this study, we defined internal resources as “all distinctive finally hypothesize,
internal capacities that bring competitive advantages including
quantitative and qualitative resources”.
Hypothesis 5: New ventures will differ in new venture performance
Based on this definition of internal resources, we hypothesize,
based on strategy, internal resources, environment
and fitting of these factors.
Hypothesis 3: Performance for a new venture will differ by its type
of internal resources that a new venture has:
New Venture Performance (NVP) = f (VS,VR, VE, α)
α: interaction of three factors, VS, VR and VE
New Venture Performance (NVP) = f (Internal Resources)
D. The Two-Way Interaction Effects of Venture Strategy, Environment or Internal Resources on New Venture Performance
It is generally known that success of new ventures depends
on not only various external factors but also internal resources.
Although new ventures have many favorable factors for success, they are still susceptible to failure if they don’t respond to
the changing environment.
Basic proposition on the existing theory of strategy is that
new ventures should operate their business in extent to internal
resources and technologies they might have in order to maximize the performance. Thus, strategy could be a means that directs the allocation of internal resources efficiently and effectively to adapt to the changing environment. Therefore, we hypothesize,
Hypothesis 4: New venture performance will be influenced by an
independent factor and interaction of them. More
specifically,
H 4-1: NVP = f(VE, VS, α),
H 4-2: NVP = f(VE, VR, α),
H 4-3: NVP = f(VR, VS, α),
α: interaction of two factors
E. The Three-Way Interaction Effects of Venture Strategy, Environment or Internal Resources on Venture Performance
Whether environment, strategy or internal resources have an
effect on the viability and success of new venture has not been
concluded yet. But many researchers have studied on the assumption that venture performance is determined by fitting of
strategy, internal resources, and environment.
According to the Sandberg’s study, for example, venture
strategy should be different by environment. He suggested in
his study that differentiation strategy is more useful in “start-
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Myeong-Cheol Park et al.
3. Sample, Variable, and Research Methodology
A. The Sample and Data Gathering Method
As described in section I, according to Biggadike, it takes
eight years for new ventures to get to profitability and 12 years
to be similar to existing company. Follow-up studies by
Hobson and Morrison [18] and MacMillan and Day [19] support Biggadike’s conclusion. In this study, based on the above
research results, the sample was selected using the following
four criteria.
The first criterion is that sample companies are independent
companies without investment of a large enterprise or existing
companies. Second criterion is that samples should belong to
IT industry, which is classified as service, equipment, software,
and supporting sector of IT. Third, new ventures should be
through 3 to 8 years passed. Fourth is that the number of employee is less than 80 employees3), and sample companies were
required to have publicly available revenue data at least for the
three previous fiscal years.
Two primary types of data gathering methods were used in
this research: mailed questionnaire and interview. As of May
2000, it is estimated that there are total 7,110 ventures in Korea
and among them 2,051 ventures belong to IT industry. The
percentage of ventures passed through 3 to 8 years is estimated
at about 29.1 %4). Of the approximately 600 new ventures that
meet the first, second and third criterion, finally 396 new ventures met the fourth criterion. Data was collected in two phases.
First, data was surveyed from 396 new ventures in the Korean
IT industry and 86 of them responded. Second, for gathering
more precise and in-depth data, six other new ventures were interviewed. In addition to that, to check the consistency and reliability of the response, we deliberately included some redundant questions in the survey. We use that kind of mechanism.
3) In Korea, ventures can be defined by number of employee(less than 80)
4) Korea Small and Medium Business Administration, Status of Small and Medium Enterprises, 2000. 5.
ETRI Journal, Volume 22, Number 4, December 2000
Table 1. The characteristics of sample ventures surveyed.
Sectors
Responded
%
Service
13
9.49 %
Equipment
28
20.44 %
Software
62
45.26 %
Supporting
33
24.09 %
Others
1
Total
0.73 %
*
137
100 %
* Since 42 companies engage in more than one business domain, total number of
companies responded (137) is greater than total sample size (92).
ternational level to corporate level.
As previously noted, the purpose of this study is to define the
venture performance model and suggest the appropriate strategies suitable for the environmental conditions and internal resources to venture entrepreneurs based on the results of testing
five hypothesis. With consideration of this purpose, the environment was operationalized to reflect these three dimensions:
Product and Market, Competition, and Technology. Also we relied on the respondent’s perception of the environment while
keeping in mind the measure’s vulnerability to bias.
3) Internal Resources
In this study, we are interested in examining the influence of
internal resources on the performance of new ventures. The inThrough these two data gathering methods, total of 92 sample ternal resource capability of the firm is a source of competitive
data was obtained. Thus, the effective response rate was 21.7 %. advantages and essential to the success of a firm’s strategy [16],
To test that the respondent sample was not biased, respondent [17]. Prior researches on the internal resources were rarely conand non-respondent firms were compared with regard to busi- ducted other than entrepreneurs or organization.
ness sector, number of employee, and years passed. One-way
In order to measure the internal resources, we should measANOVAs showed no significant difference between two groups. ure the intangible resources such as the value, rareness or imitability of new venture as well as tangible resources. But, it is
B. Operationalization of Variables
very difficult to measure the value, rareness or imitability of
new venture quantitatively [16]. We measured the amount of
1) Strategy
the physical or intellectual resources of new ventures as an inIn this study, strategy is operationalized as a means to secure dicator of internal resources. In this study, we tried to measure
competitive advantage. The operationalization has two issues all possible management functions related to internal resources
to be resolved: Theoretical and Methodological.
including entrepreneurs and organization. As with strategy and
First, the intended strategy is not always the same with the environment measure, all questions were operationalized on a
realized strategy. That is, a realized strategy may be the result of five-point scale.
intention or may be emergent with little resemblance to the
strategy that was intended [20], [21]. Thus, to avoid the concern
4. Data Analysis Techniques and Methodology
related to this theoretical issue, in this study, strategy was defined in terms of intentions, guidelines for the future – essenThe sixty-four different variables concerning strategy, envitially in terms of plan and self-reported measures were adopted ronment, internal resources and performance were measured
as the way of estimating a venture’s strategy.
on the ordinal and ratio scale. Sixty-one of those variables were
The second concern is related with the range of strategy. The independent variables, and three variables were dependent variscope of strategy, broad or narrow, should be determined by the ables. Prior theory and research in the fields of industrial orresearch purpose. Because the purpose of this study is to define ganization, strategic management, and entrepreneurship suggest
the venture performance model comprehensively, the ques- that measures of business performance based on Return On
tionnaire was constructed not to be narrow but to be broad Assets (ROA), Return On Equity (ROE), Return On Sales (ROS),
enough to cover the all business functions related to strategies and Sales Growth (SG) are the most important goals of business
such as general management, product, marketing, technology, enterprises [13]. Sandberg and Hofer chose ROE as their priand financing. The questionnaire asked the respondents to mary performance criterion [22] and Stuart and Abetti [9] chose
describe their strategy that the new venture seek to pursue em- growth in sale, growth in employment, profitability, and producphasizing a certain function of management. All questions tivity (revenue/employee) as a quantified performance measures
were operationalized on a five-point scale.
and six subjective performance measures such as meeting plan,
2) Environment
Strictly speaking, to fully understand environmental conditions facing the new ventures, it should be examined from
international level to corporate level.
ETRI Journal, Volume 22, Number 4, December 2000
employee satisfaction, overall evaluation of progress, survivability of the firm, ability to attract capital, and cash flow. This study
selected three performance-related variables—Growth Rate of
Sales (GRS), Growth Rate of Assets (GRA), and Return on In-
Myeong-Cheol Park et al.
55
vestment (ROI)—as measures of firm performance (dependent
variables).
The hypotheses were tested using a combination of factor
analysis, cluster analysis, analysis of variance, and correlational
analysis. In order to test seven hypotheses, seven different models were constructed. Factor analysis was employed for several
reasons in this study. First, it is a technique that reduces the data
to a smaller number of factors in the range of minimizing the information loss by identifying the nature of covariance among the
variables. Second, factor analysis aids in detecting the presence
of meaningful patterns among variables.
Then, the factor scores derived from factor analysis were
used as input data for the clustering analysis. The K-means
cluster method employed attempts to identify relatively homogeneous groups of cases based on the selected characteristics.
This procedure produces clusters that minimize the average
squared Euclidean distance within each cluster. There is no definitive statistical procedure for choosing the final number of
cluster. This choice is usually made by the researcher depending on the nature of the study and the objectives of the clustering [23], [24]. In this study, in consideration of the objectives of
the clustering, we chose the four clusters. In order to identify
the cluster produced by cluster analysis, multivariate analysis
of variance was used. That is, MANOVA was performed using
the three performance variables.
IV. ANALYSIS AND RESULTS
1. Factor Analysis
Factor analysis was employed for identifying independent
group variables rather than simple independent effects of each
variable. And factor scores of each derived factor were also
used as the input data into cluster analysis. There are two representative methods of factor extraction: Principal components
and Maximum likelihood. In case of Maximum likelihood
method, as its name implies, this method finds a solution by
maximizing the likelihood function5) . On the other hands, principal component method can help you to understand the underlying data structure and/or form a smaller number of uncorrelated variables. In this study, we chose the principal component
method as a method of factor extraction in consideration of
purpose of factor analysis.
There are two steps in an exploratory factor analysis. The
first step is to determine the number of underlying common
factors and the second step is to enhance our ability to interpret
the factors through rotation.
5) This method was used in Sethi and King’s research [25].
56
Myeong-Cheol Park et al.
A. Environment
10 variables used for deriving the dimension related to environment were selected from the aspect of market growth, competition, and technology. Of the 10 variables, the factor analysis
revealed 3 underlying factors with eigenvalue of greater than
one. These 3 factors account for 65 % of the total sample variance. As in Table 2, 5 of the 10 environmental variables loaded
on Factor 1, 3 variables on Factor 2, and 2 variables on Factor 3.
A description of each factor and its corresponding variables are
provided in Table 2.
Table 2. Description of environmental factors and its corresponding
variables.
Factors
Level of
Market
Competition
Environment
variables
Component
Factor 1
Factor 2
Factor 3
Number of
Competitors
0.822
0.252
5.719E-02
Entry Barriers
- 0.749
4.714E-02
- 0.123
Existence of
Competitors
0.694
9.221E-03
0.367
Level of Competition
0.688
0.307
-0.268
0.267
0.125
0.884
-2.376E-02
0.766
-6.909E-02
0.763
-7.536E-02
-0.135
0.897
-1.027E-02
0.867
Industry
- 0.536
Concentration
Difficulty in
Forecasting Change -53295E-03
of Customer Needs
Uncertainty Difficulty in Foreof
casting the Techno- -5.065E-02
Environment logy Development
Difficulty in Fore0.179
casting the Actions of
Competitors
Possibility
of Market
Growth
Industry Growth Rate -4.672E-02
Potential Market Size
0.122
Factor 1 can be characterized by environmental variables relating to the market competition such as the number of competitors and difficulties of entry or exit. Thus, we named Factor
1 as “Level of Market Competition.” Factor 2 can be characterized by “Uncertainty of Environment” and Factor 3, “Possibility of Market Growth.”
B. Strategy
28 variables related to strategy were selected from the aspect
of general management, product, marketing, technology, and
financing. Using the 28 variables, the factor analysis yields 7
factors. Derived 7 factors account for 63 % of the total sample
ETRI Journal, Volume 22, Number 4, December 2000
Table 3. Description of Strategy Factors.
Factor
Table 5. The Characteristics of clusters.
Factor 1
• Innovation
Factor 2
•
of Technology
Factor 3
• Financing
Factor 4
•
Market Familiarity
5
Factor 5
•
Market Scope
3
Factor 6
• Fitness
Factor 7
• Flexibility to
Cluster 1
(Potential
Market)
Cluster 2
(Matured
Market)
Cluster 3
(Saturated
Market)
Cluster 4
(Growing
Market)
Level of
Market
Competition
Low
High
Low
High
Possibility of
Market Growth
Very High
Low
Very Low
High
Uncertainty of
Environment
Relatively
Low
Very High
High
Low
Dimension
6
Availability of External Resources
6
4
of Products or Services
3
Market Changes
1
Table 4. Cluster membership.
Cluster &
membership
Cluster
Number of
variables loaded
Characteristics
I
II
III
IV
Missing Total
Forces
Environment
22
23
16
24
7
92
Strategy
9
18
42
16
7
92
Internal Resources
4
27
37
8
16
92
their environment, strategy, and internal resources. Furthermore,
cluster analysis introduces statistical rigor by using measures of
variance and distance to establish grouping of firms that are
similar to each other and dissimilar to firms in other clusters. In
this study, as a method of cluster analysis, quick cluster method
was used. Quick cluster is a nonhierarchical method and employs a K-means clustering algorithm. Table 4 shows that the
total of 92 sample ventures were classified into four groups
based on the factor scores generated by factor analysis.
A. Environment
variance. A description of each factor is explained in Table 3.
C. Internal Resources
Factor analysis could be used for several reasons. The major
purpose of employing factor analysis related to internal resources is to eliminate unnecessary variables and then to detect
the meaningful patterns among the variables easily. 51 variables were used for internal resources. Those variables are selected to explain all the expected functions related to internal
resources such as entrepreneur, organization, general management, accounting and financing and so on. Of the 51 variables,
the factor analysis revealed 9 factors with eigenvalue of greater
than one. But for additional tests relating to internal resources,
we used the 9 representative variables in each factor instead of
9 revealed factors. The reason for using representative variables
rather than revealed factors is that when factors are diverse and
excessive, representative variables are superior to show the difference between groups and to interpret meanings [26].
There is no definitive statistical procedure for choosing the
number of clusters and also cluster analysis does not explicitly
provide a clearly acceptable or unacceptable solution. In this
study, an objective criterion led to the selection of the four cluster solutions. It was significance of F-value: Factor 2 “Uncertainty of Environment” becomes significant when the number
of clusters was increased from three to four. Total 92 new ventures were grouped into four environment clusters. Cluster
membership and characteristics associated with the environmental factors are presented in Table 5 and each cluster was
named by its major characteristics.
The characteristics of 4 clusters are as followings:
•
•
2. Cluster Analysis
Cluster analysis was used to partition the sample firms into
several groups in terms of their characteristics of environment,
strategy and internal resources. The cluster analysis is considered a useful tool for distinguishing various firms in terms of
ETRI Journal, Volume 22, Number 4, December 2000
•
Cluster 1 (Potential Market): The level of market competition
and uncertainty of environment are low, but growth rate of
the industry is fast and potential market size is huge. So,
firms in this cluster may have many chances to create new
customer’s demand.
Cluster 2 (Matured Market): The level of market competition
and uncertainty of environment are high, but possibility of
market growth is low. This means that firms in this cluster
should carefully observe the change of customer needs and
trends of technology development to cope with the uncertainty of environment.
Cluster 3 (Saturated Market): Potential market size is low but
Myeong-Cheol Park et al.
57
•
uncertainty of environment is high. Due to these characteristics of cluster 3, it may be said that this market type is already
saturated, thus there is few additional chances to create new
demand.
Cluster 4 (Growing Market): Although uncertainty of environment is low, the level of market competition and possibility of market growth are high. So, it is expected that firms in
this cluster is in growth stage or sensitive to technology development. There are already many firms in this market and
the market is highly competitive.
High
•
Possibility of
Market Growth
seeking financial stability. In addition, these firms also attempt
to provide various products for maximizing their profits.
Cluster 4 (Technology Orientation): There are 7 firms in this
cluster. Although IT industry is technology-oriented industry,
there are only 7 firms. This seemingly paradox might be interpreted that most Korean IT ventures have recognized the
need to rely on some other competitive dimensions in building their competitive advantages. Firms in this cluster emphasize the continuous investment on R&D and keeping
their level of technology higher than their competitors. They
also emphasize the provision of better products and services
to meet the customers’ changing needs in an efficient and
timely manner.
C. Internal resources
Cluster 1
Cluster 4
High
Level of Market
Competition
Cluster 3
Cluster 2
High
76 new ventures among the 92 ventures were grouped into 4
clusters that were significantly different from one another
based on the nine internal resource-related variables. For the
number of clusters, the change of significance of F-value in individual variable was also used as a criterion as before.
•
Uncertainty
of Environment
Fig. 1. The Characteristics of clusters.
B. Strategy
The cluster analysis was used to partition the firms into strategy groups based on the nature of their emphasis on different
patterns of strategic orientation among the 7 strategy factors derived from factor analysis. Similar to clustering of environment,
an objective criterion led to the selection of the four cluster solutions as the more appropriate. It was significance of F-value:
Factors 3, 4 and 7 become significant when the number of clusters was increased from three to four (p < 0.1). Cluster memberships are presented in Table 4.
•
•
•
Cluster 1 (Pursuing Stability): There are 9 firms in this cluster.
Firms in this cluster emphasize the efficient use of external
resources through diverse methods such as outsourcing and
seeking the financial stability rather than investing on R&D
for exploiting new market.
Cluster 2 (Product-Orientation): 18 firms belong to this cluster. Firms in this cluster strongly emphasize providing better
products and services to the market. They also attempt to differentiate from other competitors by offering the products and
service that are not provided by other firms.
Cluster 3 (Pursuing Omnipotence): This cluster was found to
be more numerous than other clusters, 42 firms. New ventures in this cluster emphasize diversifying the market and
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Myeong-Cheol Park et al.
•
•
•
Cluster 1 (Organizational Flexibility): There are only 4 firms
in this cluster. Firms in this cluster have the following characteristics: i) close relationship between product/service development and sales team; ii) slim and flexible organization; iii)
short product development cycle.
Cluster 2 (Entrepreneur’s Leadership): There are 27 firms in
this cluster. Firms in this cluster have the competitiveness in
the areas such as technology, entrepreneur’s expertise, and
close relationship between entrepreneur’s prior career and
major products or services.
Cluster 3 (Marketer): There are 37 firms in this cluster. Firms
in this cluster are superior to their competitors in marketing
skills. They also have diverse markets.
Cluster 4 (Shared Financial Risk): There are 8 firms in this
cluster. Firms in this cluster generally pursue stabilization
through sound cash flows and strategic cooperation with
other firms rather than investing on the technology development and marketing on a stand-alone basis.
3. Results of Hypotheses Tests
In this section, we present the results of the hypotheses testing. As previously described, the objectives of this study are to
understand the role of strategy, environment and internal resources in a new venture, and to build on a new venture performance model. Thus, each hypothesis could correspond to an
individual venture performance model as followings:
•
•
H1: NPV = f(Environment) →Model 1
H2: NPV = f(Strategy) → Model 2
ETRI Journal, Volume 22, Number 4, December 2000
•
•
•
H3: NPV = f(Internal Resources) → Model 3
H4: NPV = f(Environment, Strategy, α) → Model 4-1
NPV = f(Environment, Internal Resources, α) →
Model 4-2
NPV = f(Internal Resources, Strategy, α) → Model 4-3
H5: NPV = f(Strategy, Internal Resources, Environment, α)
→Model 5.
Table 6. The results of the multivariate test for environment,
internal resources and strategy factors.
Effect
Environment
Internal
Resources
In order to test these hypotheses, the following steps and procedures are as selected.
•
•
•
First step is ANOVA or MANOVA test. But correlation
analysis must be performed before selecting ANOVA or
MANOVA test. If correlation exists between performance
measures, MANOVA must be used, otherwise ANOVA should
be used.
Second is to test identity between average vectors after investigating multivariate normal distribution and same covariance as a basic proposition.
If null hypothesis “all average vectors are equal” could be accepted, test ends at this point but if null hypothesis could not
be accepted, each variable should be investigated in detail to
clarify the which variables are different and how much different.
Before proceeding with the analysis of variance, as a result of
analysis of relationship between dependent variables, there is a
close relationship between performance variables such as GRS,
GRA and ROI. Thus, it is more desirable to use MANOVA
than ANOVA in this study.
A. Hypotheses 1: NPV = f(Environment): Rejected
Venture performance Model 1 predicted that new venture
performance would be a function of environment. In other
words, ventures under certain conditions would outperform
others under different conditions in their performance. In order
to test this hypothesis, a multivariate analysis of variance
6)
(MANOVA) was performed and GRS, GRA and ROI were
used for the dependent variables.
Depending upon the purpose of researcher, various values
such as Pillais’s trace, Wilks’ Lamda, Hotelling’s Trace, and
Roy’s Largest Root could be used for MANOVA test. In this
study Wilk’s Lamda was used. According to the results of multivariate test, value of Lamda was 0.844 and if converted into
F-value, it could be 1.136, which is insignificant at the 0.05
significance level (All three dependent variables were insignifi-
6) The Multivariate procedure provides regression analysis and analysis of variance for multiple dependent variables by one or more factor variables or covariates. And we can also investigate interactions between factors as well as the effects of individual factors. In addition,
the effects of covariates and covariate interactions with factors can be included.
ETRI Journal, Volume 22, Number 4, December 2000
Strategy
Value
F
Hypothesis
df
Error df
Sig.
0.844
1.136
9.000
141.307
0.341
Wilks’
0.801
Lamda
1.347
9.000
126.705
0.219
0.703
2.405
9.000
138.874
0.014
cant at the 0.05 significance level). These results of the MA
NOVA test indicated that for a given environmental condition,
firms would not differ in their performance. The results of the
MANOVA are presented in Table 6. In short, we can draw preliminary conclusions as followings:
•
•
•
•
None of the factors related to environment were significant.
This indicates that venture performance is not a function of
environment only.
Three aspects of environment such as market competition,
uncertainty of market and possibility of market growth are
only representatives of environments, but performance is not
wholly dependent on these.
Even if a venture is in the unfavorable environments, the appropriate strategy or management skills can overcome unfavorable conditions. The other case, a venture in the favorable
environments could have difficulties if it adopts an inappropriate strategy or management skills.
In short, performance of ventures in the IT industry is not dependent on the environments only. The Most important factors on performance of ventures may be strategy or internal
resources or interaction of these factors.
B. Hypotheses 2: NPV = f (Internal Resources): Rejected
Model 2 predicted that new venture performance would be a
function of internal resources which ventures have. This model
was also tested using multivariate analysis. Performance was
measured in terms of the GRS, GRA, and ROI as previously
used. The results of the MANOVA are presented in Table 6.
According to the results of multivariate test, value of Lamda
was 0.801 and it would be converted into F-value of 1.347,
which is insignificant at the 0.05 significance level. Neither of
these statistics are significant, indicating that Model 2 did not
achieve a good fit. Based on the results of MANOVA test, we
can draw preliminary conclusions as followings:
•
Internal resources can only represent the strength and weakness of the venture firm, but do not have significant influence
on venture performance.
Myeong-Cheol Park et al.
59
•
The only existence of internal resources could not be a critical
factor. Rather, an efficient allocation of resources could be
more important to venture performance.
Table 7. Results of the correlation analysis.
Environmental Conditions
C. Hypothesis 3: NPV = f(Strategy): Partially Accepted
Model 3 predicted that venture performance would be a
function of strategy. In other words, it means that performance
for a new venture will differ across the strategy types. In section IV, four strategy clusters were derived: i) Pursuing Stability,
ii) Product-Orientation, iii) Pursuing Omnipotence, iv) Technology-Orientation.
The overall fit of models shown in Table 6 are significant at
the 0.05 significance level. In the view of Scheffe statistics, two
variables – GRS, GRA – are significant at the 0.05 significance
level. Especially, the strategy cluster 1 named “Pursuing Stability” is relatively inferior to other strategy clusters in terms of
GRA and ROI.
Positive
Negative
• Factor 3: Financing
Competition of
Market
High
• Factor 6: Fitness
• Factor 6:
Low
Possibility of
Market Growth
Fitness of
Product
High
Uncertainty of
Environment
of
-
Product
• Factor 4:
Market
Familiarity
-
-
• Factor 6:
Fitness of
Product
• Factor 7: Flexibility
to Market changes
Low
High
Low
• Factor 6: Fitness
of
Product
• Factor 4:
Market
Familiarity
• Factor 4:
Market
Familiarity
• Factor 4:
Market
Familiarity
-
4. Hypothesis 4: Model 4-1, 4-2, 4-3
Model 4 is essentially Model 1, 2 and 3 with the addition of
two-factor interaction terms in the multivariate test. Model 4
was organized into 3 sub-models in order to examine not only
the individual influence of environment, internal resource or
strategy factors but also the effects of two-factor interaction.
Model 4-1: NPV = f(Environment, Strategy, α):
Partially Accepted
The purpose of Model 4-1 is to look at the influence of environmental factor, strategy factor, and interaction of two factors.
Model 4-1 is not tested within the framework of a multivariate
test. Rather, the correlation analysis was used to determine the
level of significance of the fit of the model. Model 4-1 states
that under some environmental conditions, firms with a certain
type of strategy are expected to outperform the others in terms
of GRS, GRA and ROI. The results for Model 4-1 are presented in Table 7.
Interestingly, in most cases, Factor 4 “Market Familiarity” is
negatively related to performance. But in case of new ventures
operating in environment where market is not expected to grow,
“Market Familiarity” strategy is positively related to performance measures.
Model 4-2: NPV = f(Environment, Internal resources, α):
Accepted
This model predicted that new venture performance would
be a function of environment, internal resources and environment-internal resources interaction. According to the results of
test, the overall fit of the Model 4-2, as indicated by an F-value
of 76.026 for internal resource factor and 81.716 for environ-
60
Myeong-Cheol Park et al.
Table 8. The results of the multivariate test for Environment/
Internal Resources and Interaction effect factors.
Effect
Hypothesis
Error df
df
Sig.
Value
F
Intercept
0.023
538.854a
3.000
38.000
0.000
Environment
0.005
81.716
9.000
92.633
0.000
0.006
76.026
9.000
92.633
0.000
0.002
36.424
9.000
110.813
0.000
Internal
Resources
Environment
*Internal
Resources
Wilks’
Lamda
a: the statistics is an upper bound on F that yields a lower bounds on the significance level
ment was significant. Further, adding the interaction term was
also significant at the 0.05 significance level with an F-value of
36.242.
According to these results, we can draw some preliminary
conclusions as followings:
As can be seen in Model 1 and 2, venture performance was
not influenced by factors individually. In case of considering
two factors simultaneously and adding the interaction term,
however, fit of the model was improved significantly.
This result indicates that venture performance could be improved when these two factors are fitted. In other words, venture performance is a function of matching the firm’s internal
resources to its environment.
Model 4-3: NPV = f(Internal Resources, Strategy, α):
Marginally Accepted
ETRI Journal, Volume 22, Number 4, December 2000
Model 4-3 is essentially Model 4-2 with the substitution of
strategy for environment. This model predicted that new venture performance would be a function of strategy, internal resources, and interaction of these two factors. According to the
results of test, the overall fit of the Model 4-3, as indicated by
F-value of 1.937 for internal resource, was not significant at the
0.05 significance level. However, it is marginally significant at
the 0.1 significance level. In case of individual influence of factors, two of three factors are significant at the 0.05 significance
level. Thus, we can conclude that venture performance could
be marginally a function of strategy, internal resources, and interaction of two factors.
Table 10. The results of the multivariate test for internal resources/
strategy/environment, two way and three way interaction
effect factors.
Effect
Intercept
Effect
Value
Intercept
Internal
Resources
Strategy
Internal
Resources
*Strategy
Wilks’
Lamda
F
0.788
a
3.864
3.000
43.000
0.016
0.688
1.937
9.000
104.801
0.054
0.615
2.572
9.000
104.801
0.010
0.453
2.194
18.000
122.108
0.006
a: the statistics is an upper bound on F that yields a lower bounds on the significance level
5. Hypothesis 5: NPV = f(Environment, Internal Resources,
Strategy, α): Accepted
As described in section III, the beginning model of this study
was NPV = f(VS, VR, VE). Until now, we examined the various functions, which is broken down into component part. As
can be seen from the previous analysis, Model 1, 2, and 3 are
not adequate to venture performance model and Model 4 is also not enough to explain the venture performance fully. Thus,
back to the beginning model, we now examine Model 5 including not only all factors but also all interaction terms. In order to test the final model, multivariate analysis of variance was
also performed. And GRS, GRA and ROI were used for the
dependent variables as before.
The result of this analysis is shown in Table 10.
According to Table 8, Lamda values of each factor are 0.361,
0.428 and 0.003, which are all significant at the 0.05 significance level. This indicates that venture performance would be
influenced by all these factors. Especially, in case of interaction
terms between strategy and environment, value of Wilk’s
Lamda is 0.173, which is significant at the 0.05 significance
level with an F-value of 2.856. Interaction term between three
factors is also significant at the 0.05 significance level with an
ETRI Journal, Volume 22, Number 4, December 2000
Hypoth
esis df
Error
df
Sig.
0.010 713.089a 3.000 21.000 0.000
0.361
2.958
9.000 51.259 0.007
Strategy
0.428
2.379
9.000 51.259 0.025
Environment
0.003
55.109
9.000 51.259 0.000
0.575
1.454
9.000 51.259 0.191
0.543
1.210
12.000 55.852 0.300
Environment*
Strategy
0.173
2.856
18.000 59.882 0.001
Strategy*
Environment*
Internal Resources
0.232
3.343
12.000 55.852 0.001
Environment
Sig.
F
Internal Resources
Internal Resources*
Strategy
Wilks’
Internal Resources* Lamda
Table 9. The results of the multivariate test for internal resources/
strategy and interaction effect factors.
Hypothesis
Error df
df
Value
a: the statistics is an upper bound on F that yields a lower bounds on the significance level
F-value of 3.343. Interestingly, two interactions relating to internal resources are not significant at the 0.05 significance level.
These statistics could be interpreted as following: Since new
ventures usually does not have enough internal resources compared with the already-established companies, new ventures
may not be able to use internal resources as their competitive
weapons generally.
This finding could be interpreted as supporting the general
strategy theory that when environment, internal resources, and
strategy are fitted, performance could be maximized and further, strategy is the means that make internal resources and environment fit. So, we can obtain the final venture performance
model as follows:
NVP = f(VS,VR, VE, α).
6. Summary of Tests
This section reported the results of the statistical analysis for
the five models, namely, five hypotheses developed in this study.
One of the major findings to emerge from the analysis is that individual action in the form of factor would not have a significant
effect on performance. That is, the ability of the model to predict
performance can be significantly improved by adding the interaction terms between factors.
Based on test results, we can draw conclusions as followings:
•
The venture performance in the Korean IT industry would be
influenced by internal resources, environment, strategy, and
Myeong-Cheol Park et al.
61
•
interactions of these factors.
It is confirmed empirically that an individual firm’s strategy
should be matched the firm’s resources with the environment
in order to attain maximum performance outcomes.
V. IMPLICATIONS FOR VENTURE FIRMS
Until now, three variables, environment, internal resources,
strategy, and their interaction effects between these variables,
were shown to play important roles in a new venture performance. These findings may not be so surprising in the field of entrepreneurship research although we added one more variable to
the existing model. For the first time, however, this research empirically confirms that this type of model can be extended and
made applicable to the Korean IT industry by rigorous statistical
procedures. One important implication of these findings is that
each venture firm should establish its appropriate business strategy based on the environments and internal resources that it has.
In the Korean IT industry, many different types of venture companies currently exist. They have different internal resources and
environments. How can they exploit these findings?
In this section, correlation analysis was employed to figure
out what types of strategy new ventures should adopt or avoid
in order to increase their performance while considering their
environmental conditions and internal resources. We classified
new venture firms into 24 types based on their environmental
conditions and clusters of internal resources identified in Section
IV. In each type of venture companies, the correlation analysis
between performance variables and types of strategies shows
which strategies are positively effective to their performance
and which ones are not. These results are summarized in Table
11. Let’s take an example. For the venture company where the
‘Possibility of Market Growth’ is high and its internal resources
belong to Cluster 4, the strategies such as ‘Fitness of Products
or Service’ and ‘Flexibility to Market Changes’ are found to be
effective, but ‘Innovations of Technology’ may not be effective
to increase the performance. These correlation analysis results
of this example can be interpreted as following combined with
the previous test results (See Table 5). We can assume that the
venture company where ‘Possibility of Market Growth is high’
and its internal resources belong to “Shared financial risk” may
be at the early stage such as ‘Potential Market’ or ‘Growing
Market’ in view of company life cycle. Accordingly, the best
ways to maximize the venture performance of this example
company might not be to create the new market through innovation of technology but to raise the market shares with an efficient marketing method through sound cash flow for obtaining
the first mover advantages [27]–[29].
According to the results of this analysis, there appears to be a
wide difference for effective and ineffective strategies, which
62
Myeong-Cheol Park et al.
depend on the environmental conditions and internal resources.
These results can be used for each venture firm that has unique
environment and internal resources to identify effective and ineffective types of strategies. This analysis also confirmed the
previous findings that, for a given environmental condition and
internal resources, venture firms should adopt different strategies in order to attain competitive advantages in the Korean IT
industry.
VI. SUMMARY AND CONCLUSION
1. Contributions of this Study
The purpose of this study was to contribute to the literature
on new venture study in two ways. First, we sought to investigate that what factors among the strategy, environment, and internal resources impact new venture performance and the extent to which strategy, environment and internal resources influence new venture performance in the Korean IT industry. So,
our first purpose of this study was to conduct a detailed investigation of the performance of the new ventures in the Korean IT
industry reflecting the unique characteristics that they solely
have. Our second objective was to suggest the appropriate
strategies suitable for the environmental conditions and internal
resources to venture entrepreneurs based on the test results.
In summary, three basic research questions were investigated
in this study: i) the impacts of strategy, environment, and internal resources on the Korean IT new venture performance; ii)
whether basic beginning model of strategy, environment, and
internal resources could be able to predict the Korean IT venture performance outcomes using quantitative performance
measures such as GRS, GRA, and ROI; iii) what is the appropriate strategies suitable for the given environmental conditions
and internal resources?
2. Limitations and Further Study
As with all studies, the analysis and interpretations of the findings
of the study are subject to a number of limitations. The first limitation is the inability to classify new ventures into more detailed
categories within the IT industry. For example, the software sector differs from the service sector in terms of inventory requirement, and personnel requirement, etc. Thus, they are expected to
show differences in the strategy, environment, internal resources,
and performance relationship. A second limitation is associated
with the measurement of performance. The ability of the model
to predict performance would differ by the performance measures
used. In this study, three performance measures were used: GRS,
GRA, and ROI. However, GRS, GRA, and ROI may not be true
indications of the profitability of the new ventures. Up to now,
however, it is generally accepted that the performance measures
ETRI Journal, Volume 22, Number 4, December 2000
Table 11. Effective and ineffective strategies under environmental and resource conditions.
Environment
Dimension
Condition
High
Positive
1
-
2
3
4
Level of Market
Competition
Strategy
Clusters of
Internal Resource
•
Low
3
4
High
•
Financing**
-
Flexibility to Market Changes**
-
Innovation of Technology*
• Fitness of Products or Services***
-
•
•
Flexibility to Market Changes**
Financing***
Fitness of Products or Services**
• Flexibility to Market Changes**
•
•
Availability of External Resources***
Financing***
Innovation of Technology***
Availability of External Resources***
• Market Familiarity**
•
•
•
•
2
•
Innovation of Technology**
3
•
Innovation of Technology*
•
Market Scope*
•
Fitness of Products or Services*
Flexibility to Market Changes**
•
Innovation of Technology *
2
Low
•
-
•
•
-
Innovation of Technology **
Market Scope*
-
Availability of External Resources**
Financing**
• Fitness of Products or Services**
•
3
-
•
4
-
-
1
-
-
2
-
3
4
Uncertainty of
Environment
Innovation of Technology*
•
1
High
-
1
4
Possibility of
Market Growth
•
Fitness of Products or Services**
1
2
Negative
•
Innovation of Technology**
•
Fitness of Products or Services*
Flexibility to Market Changes*
•
1
-
2
-
Low
Financing*
Market Familiarity*
• Fitness of Products or Services*
•
Innovation of Technology**
Market Familiarity**
•
Market Scope**
•
•
Innovation of Technology***
Financing*
•
Flexibility to Market Changes**
•
•
3
•
4
used in this study are more objective indicators of economic returns to entrepreneurs than others.
There are several suggestions for future studies. First, further
study needs to be done utilizing multiple and differing measures of new venture performance. Our results suggest that
ETRI Journal, Volume 22, Number 4, December 2000
-
-
models of new venture performance need to be tailored to the
type of performance being measured. Also, performance measures for new ventures need to be refined so that future studies
can be more consistent and comparable. Second, while acquiring the large and longitudinal data is time consuming and ex-
Myeong-Cheol Park et al.
63
pensive, it would be useful to investigate the relationship of
strategy, environment, internal resources, and performance.
Especially, in terms of performance, its significance may become
increasingly important.
In spite of these limitations, the aim of this study was to understand more fully determinants of new venture performance.
We expect that this study would encourage venture entrepreneurs to create new ventures in the IT industry and assist new
ventures in establishing the business strategies more suitable
for their characters.
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Myeong-Cheol Park received the B.S. degree
in Industrial Engineering, and M.S. degree in
Business Administration from Seoul National
University, Seoul, Korea, in 1976 and 1978 respectively. He received his Ph.D. degree in
Management Sciences from University of Iowa
in 1990. From 1981 to 1997, he worked for
ETRI, where he engaged in many research projects related to IT management and economic issues. Currently, he is an
associate professor at the School of Management, Information and
Communications University (ICU). His main research interests include
IT management, Strategies and venture business management.
ETRI Journal, Volume 22, Number 4, December 2000
Sang-Woo Lee received the B.S. degree in Business Administration from Sogang University,
Seoul, Korea in 1996, and M.S. degree in Business Administration from Information and
Communications University (ICU), in 2000. Currently, he is a Ph.D. student in Business Administration, ICU. From 1996 to 1998, He worked for
Ssangyong Information and Communications
Corporation. His main research interests include strategy for ventures and
IT management.
ETRI Journal, Volume 22, Number 4, December 2000
Myeong-Cheol Park et al.
65